Wikipedia

link

The Positive and Negative Affect Schedule (PANAS) is a self-report questionnaire that consists of two 10-item scales to measure both positive and negative affect. Each item is rated on a 5-point scale of 1 (not at all) to 5 (very much). The measure has been used mainly as a research tool in group studies, but can be utilized within clinical and non-clinical populations as well.[1] Shortened, elongated, and children’s versions of the PANAS have been developed, taking approximately 5–10 minutes to complete.[2] Clinical and non-clinical studies have found the PANAS to be a reliable and valid instrument in the assessment of positive and negative affect.[3]

German version and German version of panas questionnaire

sample data

source

paper

We refer to Sample 4: Sport students

Measured the last training day before a competition.

A convenience sample of 533 sports athletes from Cór-doba, Argentina (173 female, 32.5%) completed the PANAS 1 to 72 hours prior to competing (M = 3 4.9, SD = 20.51). The sample was made up of soccer players (n = 197), hockey players (n = 184), basketball players (n = 46), handball play-ers (n = 52), indoor soccer players (n = 22) and martial arts practitioners (n = 32), from 14 to 50 years old (M = 21.22, SD = 5.71). On average they trained 4.20 days per week (SD = 1.21), between 3 and 10 hours per day. Athletes overall av-eraged 10 or more years practicing their respective sports and they had all competed at province to international lev-els at least three times a year. [manches ist Unsinn]

Type of sport categories are “soccer”, “hockey”, “martial_arts”, “handball”, “indoor_soccer”

Procedure

After initial contacts, data collection was done on the last training day before competition. All other procedures were like Study 2. The questionnaires were collectively ad-ministered in the locker room, but each participant com-pleted the questionnaire individually.

dplyr

See unit dplyr.html

We use piping (%>%) here.

# we need the packages of tidyverse
require(tidyverse)

## Loading required package: tidyverse

## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
## ✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
## ✔ tidyr   0.8.3       ✔ stringr 1.4.0  
## ✔ readr   1.3.1       ✔ forcats 0.4.0

## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

# read data
# dd <- read_tsv("arg_panas_sf_20.txt")
dd <- readr::read_tsv("http://md.psych.bio.uni-goettingen.de/data/div/arg_panas_sf_533.txt")

## Parsed with column specification:
## cols(
##   .default = col_double()
## )

## See spec(...) for full column specifications.

# we check for ranges
# apply(dd, 2, range)
# dd %>% summarise(mean = mean(.), min=min(), max=max(), n = n())
# erg <- dd %>% select(age:afraid) %>% summarise_all(funs(mean, min, max))
# View(erg)

# as always it's a good idea to look for the range
# we want to see all columns
dd %>% select(age:afraid) %>% summarise_all(funs(min)) %>% print(width = Inf)

## Warning: funs() is soft deprecated as of dplyr 0.8.0
## please use list() instead
## 
## # Before:
## funs(name = f(.)
## 
## # After: 
## list(name = ~f(.))
## This warning is displayed once per session.

## # A tibble: 1 x 19
##     age days_till_match hours_till_match n_type_of_sport
##   <dbl>           <dbl>            <dbl>           <dbl>
## 1    14               0                1               1
##   weekly_training_days daily_training_hours training_years matches_per_year
##                  <dbl>                <dbl>          <dbl>            <dbl>
## 1                    2                    1              1                1
##   level_in_matches active upset hostile inspired ashamed alert nervous
##              <dbl>  <dbl> <dbl>   <dbl>    <dbl>   <dbl> <dbl>   <dbl>
## 1                1      1     1       1        1       1     1       1
##   determine attentive afraid
##       <dbl>     <dbl>  <dbl>
## 1         1         1      1

dd %>% select(age:afraid) %>% summarise_all(funs(max)) %>% print(width = Inf)

## # A tibble: 1 x 19
##     age days_till_match hours_till_match n_type_of_sport
##   <dbl>           <dbl>            <dbl>           <dbl>
## 1    50               3               72               6
##   weekly_training_days daily_training_hours training_years matches_per_year
##                  <dbl>                <dbl>          <dbl>            <dbl>
## 1                    9                    4              9                9
##   level_in_matches active upset hostile inspired ashamed alert nervous
##              <dbl>  <dbl> <dbl>   <dbl>    <dbl>   <dbl> <dbl>   <dbl>
## 1                9      9     9       9        9       9     9       9
##   determine attentive afraid
##       <dbl>     <dbl>  <dbl>
## 1         9         9      9

# alternatively
# dd %>% summarise_at(vars(age:afraid), max) %>% print(width = Inf)
# select columns by excluding those we don't need and use the rest
# dd %>% summarise_at(vars(-id, -n_gender), max) %>% print(width = Inf)


# there are strange values
# we take a closer look by applying a command to all columns
# this is nothing that has to do with dplyr
# apply(dd[,7:ncol(dd)], 2, table)

# we have probably an old style SPSS data file and 9 specifies missings
# we set all 9 in suspected columns to NA

dd  %>% select(age:afraid) %>% mutate_all(~ replace(.x, .x == 9, NA))

## # A tibble: 533 x 19
##      age days_till_match hours_till_match n_type_of_sport weekly_training…
##    <dbl>           <dbl>            <dbl>           <dbl>            <dbl>
##  1    25               2               48               3                5
##  2    22               2               48               3                3
##  3    16               2               48               3                6
##  4    29               2               48               3                6
##  5    15               2               48               3                6
##  6    26               2               48               3                5
##  7    22               2               48               3                3
##  8    21               2               48               3                3
##  9    16               2               48               3                3
## 10    19               2               48               3                3
## # … with 523 more rows, and 14 more variables: daily_training_hours <dbl>,
## #   training_years <dbl>, matches_per_year <dbl>, level_in_matches <dbl>,
## #   active <dbl>, upset <dbl>, hostile <dbl>, inspired <dbl>,
## #   ashamed <dbl>, alert <dbl>, nervous <dbl>, determine <dbl>,
## #   attentive <dbl>, afraid <dbl>

# this replaces the existing tibble dd
dd <- dd %>% mutate_at(.vars = vars(active:afraid),
             .funs = funs(ifelse(. == 9, NA, .)))

# we convert numeric coded grouping variables to type factor and give appropriate labels to factor levels 
dd <- dd %>% mutate(gender = factor(n_gender,
  levels = c(1,2),
  labels = c("female", "male")))

dd <- dd %>% mutate(type_of_sport = factor(n_type_of_sport,
  levels = c(1,2,3,4,5),
  labels = c("soccer", "hockey", "martial_arts", "handball", "indoor_soccer")))


# we calculate the positive and negative affect as mean value of the appropriate variables
# we use function rowMeans() for that and select the adequate variables
dd <- dd %>% mutate(p_aff = rowMeans(select(., c(active, inspired, alert, determine, attentive))))
dd <- dd %>% mutate(n_aff = rowMeans(select(., c(upset, hostile, ashamed, nervous, afraid))))

Instead of factor() which applies to nominal data, we have ordered() for ordered data, f. e. low, medium, high.